Boostapp MCP Server for Pydantic AIGive Pydantic AI instant access to 1 tools to Create Lead

Name: Boostapp
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Boostapp through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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The Boostapp MCP Server for Pydantic AI is a standout in the Sales Automation category — giving your AI agent 1 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Boostapp "
            "(1 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Boostapp?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Boostapp MCP Server

Connect your Boostapp account to any AI agent to streamline your sales and customer acquisition workflows. This integration allows your AI to act as a virtual sales assistant, capturing lead information and organizing it within your CRM through natural conversation.

Pydantic AI validates every Boostapp tool response against typed schemas, catching data inconsistencies at build time. Connect 1 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Lead Creation — Instantly create new leads with full names and contact numbers directly in the Boostapp system.
Pipeline Management — Automatically assign leads to specific pipeline stages and identify lead sources using system IDs.
Detailed Profiling — Capture comprehensive lead data including email addresses, birth dates, gender, and custom remarks.
Subscription Integration — Link new leads to specific items or subscriptions during the creation process.

The Boostapp MCP Server exposes 1 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 1 Boostapp tools available for Pydantic AI

When Pydantic AI connects to Boostapp through Vinkius, your AI agent gets direct access to every tool listed below — spanning crm, lead-management, sales-pipeline, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

create

Create lead on Boostapp

Requires full name and phone number. Can optionally include pipeline stage and subscription details. Create a new lead in Boostapp

Connect Boostapp to Pydantic AI via MCP

Follow these steps to wire Boostapp into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 1 tools from Boostapp with type-safe schemas

Why Use Pydantic AI with the Boostapp MCP Server

Pydantic AI provides unique advantages when paired with Boostapp through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Boostapp integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Boostapp connection logic from agent behavior for testable, maintainable code

Boostapp + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Boostapp MCP Server delivers measurable value.

Type-safe data pipelines: query Boostapp with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Boostapp tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Boostapp and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Boostapp responses and write comprehensive agent tests

Example Prompts for Boostapp in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Boostapp immediately.

"Create a new lead for John Smith with phone 0501234567 and email john@example.com."

"Add a lead named Sarah Connor, phone 0549876543, and set her pipeline stage to 2."

"Register a lead for Mike Ross (0521112233) with the remark 'Interested in the annual subscription' and item ID 101."

Troubleshooting Boostapp MCP Server with Pydantic AI

Common issues when connecting Boostapp to Pydantic AI through Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Boostapp + Pydantic AI FAQ

Common questions about integrating Boostapp MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Boostapp MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.